Model reference adaptive control based on reinforcement learning

Aiming at adaptive control problems of a sort of nonlinear system,model reference adaptive control based on reinforcement learning is proposed.The controller uses adaptive heuristic critic algorithm,which consists of two elements:adaptive critic element,associative search element.The desired performance index is presented by the reference model,and the controller parameters are updated by reinforcement signal given by system.The simulation shows that the proposed method is efficient for a class of complex nonlinear system,and it has a high learning rate,which is important to online learning.